Classifying Hopf algebras of a given dimension
Bibliographic record
Abstract
Classifying all Hopf algebras of a given finite dimension over C \mathbb {C} is a challenging problem which remains open even for many small dimensions, not least because few general approaches to the problem are known. Some useful techniques include counting the dimensions of spaces related to the coradical filtration in D. Fukuda (Glasg. 2008), N. Andruskiewitsch and S. Natale (2001), M. Beattie and S. Dăscălescu (2004), studying sub- and quotient Hopf algebras in G.A. Garcia (2005), G.A. Garcia and C. Vay (2010), especially those sub-Hopf algebras generated by a simple subcoalgebra in S. Natale (2002), working with the antipode in S-H. Ng (2002), (2004), (2005), (2008), and studying Hopf algebras in Yetter-Drinfeld categories to help to classify Radford biproducts in Y-l. Cheng and S-H. Ng (2011). In this paper, we add to the classification tools in M. Beattie and G.A. Garcia (to appear) and apply our results to Hopf algebras of dimension r p q rpq and 8 p 8p where p , q , r p,q,r are distinct primes. At the end of this paper we summarize in a table the status of the classification for dimensions up to 100 100 to date.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".